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I think that's a dreadful article. We do know how neural networks work; they're a bunch of hierarchical probabilistic calculations that are pipelined. I don't really see how that couldn't work well; it's just hard to find the right probabilities. The difficulty is far more in the training than the working, and that's where the deep learning advances come in - inferring more parameters in a deeper hierarchy.

There's no relationship between a hierarchy of probabilistic estimations and a hierarchical decomposition of the cosmos. The cosmos forms an apparent hierarchy because of the rules that govern matter and the initial expansion of the universe. That a small number of parameters might be listed in describing both is neither here nor there. A small number of parameters describe the vectors in a font file. It doesn't follow that a typeface then has any relationship with my brain or the universe.

The article reads, to me, like this: neural networks are this cool hierarchy thing, the cosmos is this cool hierarchy thing, and both of these things have low Kolmogorov complexity, isn't it amazing that our brains are like this and can understand the universe, wow.

> a bunch of hierarchical probabilistic calculations that are pipelined

That's one way of describing quantum theory; generally "contextual" or "non-commuting" are used instead of "hierarchical".

If the universality of such a common framework doesn't seem profound to you, at least realise it isn't something generally appreciated and barely even hinted at just a few decades ago.

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